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The other new problems arises during the testing of such component integration that were made or used in another product. Sometimes the components may not work for certain requirements due to developed for another context. In such cases the fixing of problem after deployment increases cost of maintenance, waste of time and efforts. The component providers of COTS will provide less information in the design document of the reusable component and do not reveal the complete information about the behavior of component in the other environment. These are the new problems arise in the Component based software development compare to the traditional softwareengineering. The softwaretesting teams implement strict testing criteria and various strategies to overcome these problems of invisible code of reused components. To ensure quality of overall product testing of each component is mandatory. But problems arise during testing, when the tester has limited knowledge about the component..
• Students work steadily on a work product. Students often need milestones to assist them with time management. In previous courses students have been able to leave assignment work until the week (in some cases the day) it is due. Since the students need to show their work to peers before it is actually due for submission, they are required to begin working on it much earlier. The testing sessions serve as internal project milestones. Even though it is totally up to the individuals or teams to decide what they will have ready for a testing session large proportions of work products are implemented and/or integrated two weeks ahead of the due date, allowing them time to do some internal team testing before giving it to the testing team. The testing sessions provide an opportunity for all students to see the progress of other teams and compare their own progress.
Testing is essential in modern software development, and a technical support organiza- tion to answer customers’ questions is necessary. Documentation must be clear, complete, and easy to use for both the first-time user who is learning the software and the experi- enced user who wishes to improve his or her productivity by using the advanced features of the package. Thus, the sheer pressure of time and system size almost always requires multiple individuals in a software project. If development of a software product that fills a need requires six months, multiplication by Brooks’s conservative factor of eight means that the software will take at least four years to be ready for release, assuming that the time needed and the effort needed in terms of person-hours scale up in the same way. Of course, the product may be irrelevant in four years, because the rest of the software industry is not standing still. (This is one reason that agile software processes have become more popular.)
Barry Boehm, TRW Professor of SoftwareEngineering at University of Southern California, has defined a so-called Spiral Model. This model aims at accommodating both the waterfall and the iterative model. The model con- sists of a set of full cycles of development, which successively refines the knowledge about the future product. Each cycle is risk driven and uses proto- types and simulations to evaluate alternatives and resolve risks while produc- ing work products. Each cycle concludes with reviews and approvals of fully elaborated documents before the next cycle is initiated.
There are parallels between the use of MT in testing and its use in other softwareengineering techniques. In the context of testing, for instance, a single test case and its corresponding pass/fail outcome in test result verification relate to an MG and the corresponding MR satisfaction/violation. However, there are some challenging differences when MT is applied in other contexts. A main aim of softwaretesting is to reveal a fault, which, in MT, can be indicated by the violation of an MR. Once an MR is violated, the major task of testing has been fulfilled — it does not matter too much which test cases in the MG are actually related to the fault. In contrast, failure detection is only the starting point in some softwareengineering areas such as debugging. Precise knowledge of which test cases are failure-causing may be necessary to be able to proceed, such as with debugging , fault localization [3, 92], fault tolerance , and program repair . This is not a problem for conventional techniques that use single test cases for verification — the pass/fail outcomes simply correspond to the non-failure-causing/failure-causing test cases, respectively. However, with an MR violation, it is only possible to say that at least one test case in the MR-violating MG is related to the fault, unless we do have a test oracle. It is not clear precisely which test case is related. Such a precision problem is an intrinsic characteristic of MT, and is therefore an unavoidable cost when MT is used to address the oracle problem for other softwareengineering techniques. Consider, for example, fault tolerance techniques. Traditionally, because of the assumption of an oracle’s existence, once an input causes an incorrect output, a fault tolerance mechanism is applied to provide an alternative correct output. To address the oracle problem in fault tolerance, one simple strategy of metamorphic fault tolerance  works as follows: Multiple inputs are first constructed according to some equality MRs, and then executed simultaneously. Next, the associated outputs are verified against the MRs to decide whether or not the original input (source input in the MT context) results in a “trustworthy” output (in terms of its correctness). If the original output is regarded as untrustworthy, the most trustworthy output is selected from all the outputs associated with the follow-up inputs. A naive mechanism for metamorphic fault tolerance is shown in the following example.
SAS ® software provides a complete set of application development tools for building stand-alone, client-server, and Internet-enabled applications, and SAS Institute provides excellent training in using their software. But making it easy to build applications can be a two-edged sword. Not only can developers build powerful, sophisticated applications, but they can also build applications that frustrate users, waste computer resources, and damage the credibility of both the developer and SAS software. Formal testing will help prevent bad applications from being released, but SAS Institute offers little guidance related to softwaretesting. For those unfamiliar with the topic, this paper can serve as a primer or first step in learning about a more formal, rigorous approach to softwaretesting. The paper does not address any specific SAS product and may be appropriate for even experienced application developers. INTRODUCTION
Once the testers and developers are on the same “team,” an organization can progress to real Level 4 testing. Level 4 thinking defines testing as a mental disci- pline that increases quality. Various ways exist to increase quality, of which creating tests that cause the software to fail is only one. Adopting this mindset, test engi- neers can become the technical leaders of the project (as is common in many other engineering disciplines). They have the primary responsibility of measuring and im- proving software quality, and their expertise should help the developers. An analogy that Beizer used is that of a spell checker. We often think that the purpose of a spell checker is to find misspelled words, but in fact, the best purpose of a spell checker is to improve our ability to spell. Every time the spell checker finds an incorrectly spelled word, we have the opportunity to learn how to spell the word correctly. The spell checker is the “expert” on spelling quality. In the same way, level 4 testing means that the purpose of testing is to improve the ability of the developers to pro- duce high quality software. The testers should train your developers.
The widespread use of social engineering is undeniable. In the few years alone, we have seen social engineering play a critical role in a number of high profile attacks such as in the government-sponsored attacks that have crippled a highly secure nuclear reactor in Iran 5 and amateur attacks that have been responsible for the leakage of over 500,000 client records by a cloud application provider 6 . These attacks clearly show that in many cases, exploiting the human mind is easiest way to breach an organization’s defenses and that social engineering has made IT security a pervasive problem that cannot simply be solved through the provision of hardware or software.
Chapter 1 is a general introduction that introduces professional softwareengineering and defines some softwareengineering concepts. I have also written a brief discussion of ethical issues in softwareengineering. I think that it is important for software engineers to think about the wider implications of their work. This chapter also introduces three case studies that I use in the book, namely a system for managing records of patients undergoing treatment for mental health problems, a control system for a portable insulin pump and a wilderness weather system. Chapters 2 and 3 cover softwareengineering processes and agile devel- opment. In Chapter 2, I introduce commonly used generic software process models, such as the waterfall model, and I discuss the basic activities that are part of these processes. Chapter 3 supplements this with a discussion of agile development methods for software engineer- ing. I mostly use Extreme Programming as an example of an agile method but also briefly introduce Scrum in this chapter.
Operational testing by potential and/or existing users/customers at an external site not otherwise involved with the developers, to determine whether or not a component or system satisfies the user/customer needs and fits within the business processes. Beta testing is often employed as a form of external acceptance testing for off-the-shelf software in order to acquire feedback from the market.
In the previous two chapters we have seen first FOL and then a version of it that was slightly changed with respect to notation and number of features in the language (easier, and less, respectively), being the DL family of languages. They haven’t gotten us anywhere close to implementations, however. This is set to change in this chapter, where we will look at ‘implementation versions’ of DLs that have rich tooling support. We will take a look at the computational use of DLs with a so-called serialization to obtain computer-processable versions of an ontology and automated reasoning over it. The language that we will use to serialise the ontology is the most widely used ontology language for computational purposes, being the Web Ontology Language OWL. OWL was standardised first in 2004 and a newer version was standardised in 2009, which has fuelled tool development and deployment of ontologies in ontology-driven information systems. OWL looks like yet another a language and notation to learn, but the ones that we will consider (the DL-based ones) have the same underlying principles. It does have a few engineering extras, which also has as consequence that there are several ways to serialise the ontology so as to cater for software developers’ preferences. Thus, theoretically, there is not really anything substantially new in this chapter, but there will be many more options and exercises to practically engage with the ontology languages, automated reasoning, and toy ontologies to play with on the computer. Depending on your interests, things start to get ‘messy’ (for a theoretician) or finally concrete (for an engineer).
Afterward, an overview of the DL-based OWL 2 languages is provided in Sec- tion 4.2. Yes, plural; that’s not a typo. As we shall see, there are good reasons for it both from a computational viewpoint for scalable implementations and to please the user-base. The computational aspect is summarised in Section 4.2.4. If you have completed a course on theory of computation, this will be easy to follow. If not, you would want to consult Appendix C, which provides an explanation why one cannot have it all, i.e., both a gazillion of language features and good perfor- mance of an ontology-driven information system. Experience has seen that that sort of trade-off can annoy a domain expert become disappointed with ontologies; Section 4.2.4 (and the background in Appendix C) will help you explain to domain experts it’s neither your fault nor the ontology’s fault. Finally, OWL does not exist in isolation—if it were, then there would be no tools that can use OWL ontologies in information systems. Section 4.3 therefore sets it in context of the Semantic Web—heralded as a ‘next generation’ World Wide Web—and shows that, if one really wants the extra expressiveness, it can fit in another logic framework and system even up to second order logic with the Distributed Ontology, Model, and Specification Language (DOL) and its software infrastructure.
One test that is not included in this basic set is one that tests the signal action itself. The first if() statement (line 13) is setting up the ability for the sys- tem to respond to a Ctrl^C asynchronously. When a Ctrl^C is inputted through the keyboard, and the signal is not set to be ignored (i.e., the application is run- ning in the foreground), it removes the last hex digit that was inputted. This allows a user to correct a mistake if they entered an incorrect value. Interestingly enough, a serious defect exists with this signal-handling code. This points out very well that you cannot blindly perform white-box testing based solely on our ideas of coverage. You must understand all of the possible ways the code may be executed, both synchronously and asynchronously.
nce upon a time there was a British software house. It had a won a Command, Control, and Communication project with a police force which lived in the shadow of Hadrian’s Wall. It was a big contract, bigger than anything the software house had ever done before. But they had done lots of other C3 contracts before (well, three), and so this one wasn’t going to be any more difficult. So they wrote specifications, on paper, with pencils (makes them easier to correct). The planners of the project had not envisaged the number of terminals required (this was in an age before PCs, but after the invention of the word processor). As the project “progressed,” the numbers of developers dwindled from 365 at the rate of about 1 per day. There was a large number of tests written by a dedicated test team. Fairly early on they started testing, and found, unsurprisingly, a large number of bugs.
changes in emphasis. There are several areas where most differences occur, for example regarding the test basis. A 'catching-up' operation is frequently required when systems are maintained. Specifications are often 'missing', and a set of testware relating to the specifications simply does not exist. It may well be possible to carry out this catching-up oper- ation along with testing a new maintenance release, which may reduce the cost. If it is impossible to compile any specifications from which test cases can be written, including expected results, an alternative test basis, e.g. a test oracle, should be sought by way of compromise. A search should be made for documentation which is closest to the specifications and which can be managed by developers as well as testers. In such cases it is advis- able to draw the customer's attention to the lower test quality which may be achieved. Be aware of possible problems of 'daily production'. In the worst case nobody knows what is being tested, many test cases are execut- ing the same scenario and if an incident is found it is often hard to trace it back to the actual defect since no traceability to test designs and/or requirements exists. Note that reproducibility of tests is also important for maintenance testing.
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In order to obtain the study objective, this study selected 09(these are called as S1 until S9 in this study –) highly cited articles from the journal ―IEEE transactions on SoftwareEngineering‖ between the years 2008 and 2018. The journal is highly reputed in the study domain. The citation based study selection had basically two benefits: (1) citation makes the article’s acceptance which means that article has clear understanding and (2) to reduce the selection bias. Moreover, all of the selected articles were reviewed based on the study measure ―create a research space in softwareengineering (CARSSE)‖. The measure was developed based on the CARS model’s attributes. Each attribute of the model was quantified based on the parameters: number of words, number of sentences, tense type, number of paragraphs, and number of references. These simple frequency quantifications had helped us to summarize the general structure of the highly reputed articles which discloses the working weight against each attribute. The number of references also helped us to direct our efforts toward the best practices. In a nutshell, the parameters which were focused are paragraphs, sentences and references.